Frequent Itemsets Mining (FIM) is the basis of Association Rule Mining (ARM), and has been widely applied in marketing data analysis, protein sequences, web logs, text, music, stock market, etc. Many FIMI (FIM Implementation) algorithms have been proposed in the literature. The frequent pattern tree (FP-tree) algorithm is one of the fastest and most widely used FIMI algorithms. The FP-tree algorithm has two phases. In the FP-tree build phase, the FP-tree algorithm builds a FP-tree from a transaction database, removing all the infrequent items. In the FP-growth phase, the FP-tree algorithm grows the FP-tree by iterating through each item in the FP-tree. In particular, the FP-tree algorithm finds all the frequent items in a conditional pattern base for an item, and then builds a new FP-tree for this conditional pattern base when the conditional pattern base has at least two frequent items. Thus, the conditional pattern base is scanned twice in each iteration. In general, an FP-tree node is accessed many times since the condition pattern bases of all items share the same FP-tree.